Virtus Ciserano Bergamo: Squad, Achievements & Stats in Serie D Lombardy

Overview / Introduction about Virtus Ciserano Bergamo

Virtus Ciserano Bergamo is a football team based in the Lombardy region of Italy. Competing in the regional leagues, the team showcases a blend of local talent and strategic play under their current coach. Known for their dynamic formation, they offer an exciting prospect for fans and bettors alike.

Team History and Achievements

Virtus Ciserano Bergamo has a rich history marked by several notable seasons. While they have yet to clinch major national titles, their consistent performance in regional leagues has earned them recognition. Key achievements include multiple league top finishes and impressive cup runs that highlight their competitive spirit.

Current Squad and Key Players

The current squad features a mix of experienced players and promising young talents. Key players include:

• Marco Rossi – Striker known for his sharp goal-scoring ability.
• Giovanni Bianchi – Midfield maestro with exceptional passing skills.
• Alessandro Verdi – Defender renowned for his tactical awareness.

Team Playing Style and Tactics

Virtus Ciserano Bergamo employs a flexible 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in fast-paced attacking play and solid defensive organization, though they occasionally struggle against teams with strong aerial presence.

Interesting Facts and Unique Traits

The team is affectionately known as “The Lions of Lombardy,” reflecting their fierce playing style. They have a passionate fanbase that supports them through thick and thin, with local rivalries adding to the excitement of each match.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Marco Rossi – Top goal scorer this season.
• ❌ Alessandro Verdi – Recently sidelined due to injury.
• 🎰 Giovanni Bianchi – Consistent performer with high assist rates.
• 💡 Team’s average possession rate – 58% (League High).

Comparisons with Other Teams in the League or Division

Virtus Ciserano Bergamo often compares favorably against other regional teams due to their balanced squad and strategic gameplay. While some teams boast star-studded lineups, Virtus maintains competitiveness through teamwork and tactical discipline.

Case Studies or Notable Matches

A standout match was their recent victory against a top-tier opponent where strategic substitutions turned the game around in the final minutes, showcasing the team’s depth and resilience under pressure.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Statistic	Last Season	This Season (to date)
Total Goals Scored	45	30*
Total Goals Conceded	28	20*

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

To maximize betting potential on Virtus Ciserano Bergamo:

• Analyze head-to-head records against upcoming opponents to identify patterns.
• Maintain awareness of player injuries that may impact team performance.
• Leverage recent form trends to gauge confidence levels entering key matches.</li
  <>: Hi there! I’m working on implementing an attention mechanism using TensorFlow Probability distributions. The idea is to create an attention function that uses categorical distributions for both keys (k) and values (v). However, I’m running into some issues.

  Here’s my current code:

  python
  import tensorflow_probability as tfp

  def attention(k, v):
  “””
  k: m-dim key
  v: n-dim value
  returns: k-dim vector
  “””
  p = softmax(k)
  return tf.einsum(‘ij,jk->ik’, p[:, tf.newaxis], v)

  # Example usage:
  k = tf.constant([0.1, 0.9])
  v = tf.constant([[1.0], [0.5]])
  result = attention(k, v)
  print(result)

  And here’s the error traceback I’m getting:

  Traceback (most recent call last):
  File “attention.py”, line 14, in
  result = attention(k, v)
  File “attention.py”, line 8, in attention
  return tf.einsum(‘ij,jk->ik’, p[:, tf.newaxis], v)
  tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal but are X[0] = Y[1] [Op:Einsum]

  I expected `tf.einsum` to compute a weighted sum over `v` using `p` as weights derived from `k`, but it seems like there’s a mismatch in dimensions somewhere. I’m not sure how to resolve this issue. Could you help me understand what’s going wrong here? Thanks!
  <>: python
  It looks like there’s a dimension mismatch issue with your `tf.einsum` operation. The error suggests that the dimensions specified don’t align properly for matrix multiplication.

  Here’s a breakdown of what might be happening:

  1. **Softmax Output**: The `softmax(k)` function will output a tensor of shape `(m,)`, where `m` is the length of `k`. When you add `[:, tf.newaxis]`, it becomes `(m, 1)`.

  2. **Einsum Operation**: In your einsum notation `’ij,jk->ik’`, it expects:
  – The first dimension of `p[:, tf.newaxis]` (which is `(m,)`) to match one of the dimensions of `v`.
  – Since `v` is shaped `(n, d)`, you need to ensure that `m == n`.

  To fix this, ensure that the length of `k` matches one dimension of `v`. If you want each element in `k` to correspond to each row in `v`, make sure they have compatible shapes.

  Here’s a revised version:

  python
  import tensorflow as tf
  import tensorflow_probability as tfp

  def softmax(x):
  e_x = tf.exp(x – tf.reduce_max(x))
  return e_x / e_x.sum()

  def attention(k, v):
  “””
  k: m-dim key
  v: m x n-dim value matrix
  returns: n-dim vector
  “””
  p = softmax(k)
  return tf.einsum(‘i,i…->…’, p, v)

  # Example usage:
  k = tf.constant([0.1, 0.9])
  v = tf.constant([[1.0], [0.5]])
  result = attention(k, v)
  print(result)

  This should resolve the dimension mismatch by ensuring that each element in `k` corresponds directly to each row in `v`. Let me know if this helps! 😊

  <>: Got it! Now I want to extend this by using categorical distributions from TensorFlow Probability for both keys (k) and values (v). This should allow me to sample from these distributions during training or inference.

  Here’s what I’m thinking:

  python
  import tensorflow_probability as tfp

  tfd = tfp.distributions

  def attention_with_sampling(k_logits, v_logits):
  # Create categorical distributions from logits
  k_dist = tfd.Categorical(logits=k_logits)
  v_dist = tfd.Categorical(logits=v_logits)

  # Sample from these distributions
  sample_k_indices = k_dist.sample()
  sample_v_indices = v_dist.sample()

  # Use sampled indices to gather elements from k and v
  sampled_k = tf.gather(k_logits.exp(), sample_k_indices)
  sampled_v = tf.gather(v_logits.exp(), sample_v_indices)

  # Compute weighted sum using sampled values
  weighted_sum = sampled_k * sampled_v

  return weighted_sum

  # Example usage:
  k_logits = tf.constant([0.1, 0.9])
  v_logits = tf.constant([[1.0], [0.5]])
  result = attention_with_sampling(k_logits, v_logits)
  print(result)

  Does this approach make sense? How can I ensure that the sampling aligns correctly with my intended logic?
  <>: Your approach makes sense for incorporating sampling into your attention mechanism using TensorFlow Probability distributions. Here are some considerations to ensure alignment with your intended logic:

  1. **Sampling Indices**: When you sample indices from categorical distributions (`sample_k_indices` and `sample_v_indices`), these indices are used to gather elements from your logits after applying exponentiation (`exp()`). Ensure that these indices are within valid bounds.

  2. **Gathering Elements**: You’re gathering elements based on sampled indices from both keys (`k`) and values (`v`). Make sure that both tensors have compatible shapes after sampling.

  3. **Weighted Sum Calculation**: The multiplication (`weighted_sum`) assumes element-wise operations between sampled keys and values.

  Here’s an adjusted version considering these points:

  python
  import tensorflow as tf
  import tensorflow_probability as tfp

  tfd = tfp.distributions

  def attention_with_sampling(k_logits, v_logits):
  # Create categorical distributions from logits
  k_dist = tfd.Categorical(logits=k_logits)

  # Sample index for key selection
  sample_k_index = k_dist.sample()

  # Gather corresponding value logits based on sampled key index
  selected_v_logit_row_index = sample_k_index % len(v_logits)
  selected_v_logit_col_index_range_start = sample_k_index // len(v_logits) * len(v_logits[0])
  selected_v_logit_col_index_range_end= selected_v_logit_col_index_range_start + len(v_logits[0])

  # Flatten v logits for easy indexing then gather based on calculated indices range
  flat_v_logits=tf.reshape(v_logits,[len(v_logits)*len(v_logits[0])])
  selected_v_logits=flat_v_logits[selected_v_logit_col_index_range_start:selected_v_logit_col_index_range_end]

  # Exponentiate logits before gathering elements for numerical stability
  sampled_k_value=tf.gather(tf.exp(k_logits), sample_k_index)
  sampled_v_values=tf.gather(tf.exp(selected_v_logits),tf.range(selected_v_logit_col_index_range_start,len(flat_v_logits),len(v_logits)))

  # Compute weighted sum using sampled values
  weighted_sum=tf.reduce_sum(sampled_k_value*sampled_v_values)

  return weighted_sum

  # Example usage:
  k_logits=tf.constant([0.1 ,0 .9])
  v_logsits=tf.constant([[1., .8],[ .7 , .6]])
  result=attention_with_sampling(k_logsits,v_logsits )
  print(result.numpy())

  This ensures proper alignment by handling multi-dimensional indexing carefully when sampling from both keys and values.
  <>: Alright! Now I want to refine this by allowing batch processing so I can handle multiple sets of keys and values simultaneously during training or inference. How can I modify the function to support batches? Here’s what I have so far:

  python
  import tensorflow as tf
  import tensorflow_probability as tfp

  tfd = tfp.distributions

  def batch_attention_with_sampling(batch_k_logits, batch_v_logits):
  # Create categorical distributions from logits for each batch item

  batch_size= batch_k_logsits.shape[0]
  results=[]
  for i in range(batch_size):

  k_dist=tfd.Categorical(logits=batch_k_logsits[i])
  sample_k_index=k_dist.sample()

  selected_v_logit_row_index=sample_k_index % len(batch_vlogits[i])
  selected_vlogit_col_index_range_start=sample_k_index // len(batch_vlogits[i]) * len(batch_vlogits[i][0])
  selected_vlogit_col_index_range_end= selected_vlogit_col_index_range_start + len(batch_vlogits[i][0])

  flat_vlogits=tf.reshape(batch_vlogits[i],[len(batch_vlogits[i])*len(batch_vlogits[i][0])])
  selected_vlogits=flat_vlogits[selected_vlogit_col_index_range_start:selected_vlogit_col_index_range_end]

  sampled_k_value=tf.gather(tf.exp(batch_k_logsits[i]), sample_k_index)
  sampled_vlog_values=tf.gather(tf.exp(selected_vlogits),tf.range(selected_vlogit_col_index_range_start,len(flat_vlogits),len(batch_vlogits[i])))

  weighted_sum=tf.reduce_sum(sampled_k_value*sampled_vlog_values)

  results.append(weighted_sum)

  return results

  # Example usage:
  batch_k_logsists=tf.constant([[0 .1 , .9],[ .3 , .7]])
  batch_ vow_logsist=tf.constant([[[1., .8],[ .7 , .6]], [[ .5 , .4],[ .3 , .2]]])
  result=batch_attention_with_sampling(batch_ k_logsist,batch_ vow_logsist )
  print(result.numpy())

  How can I ensure this works efficiently across batches?
  <>: To efficiently handle batch processing while maintaining clarity and performance, you can leverage TensorFlow operations without explicit Python loops where possible:

  python
  import tensorflow as tf
  import tensorflow_probability as tfp

  tfd = tfp.distributions

  def batch_attention_with_sampling(batch_k_logits, batch_v_logits):
  # Get batch size dynamically from input shape.
  batch_size=batch_k_labels.shape[0]

  # Create categorical distribution for each item in the batch.
  k_dists=tfd.Categorical(logits=batch_ klgsitis)

  # Sample indices from these distributions.
  samples=k_dists.sample()

  # Calculate row indices for selecting value logits.
  selected_row_indices=samples % batch_vocab_size

  # Calculate column start indices based on row selections.
  col_start_indices=(samples // vocab_size_per_batch) * vocab_size_per_batch

  # Flatten value logits along last two dimensions.
  flat_vocab_tensor=tf.reshape(batch_vocablogts,[batch_size,-1])

  # Gather selected value logits using calculated indices.
  selected_vocab_slices=tf.batch_gather(flat_vocab_tensor,col_start_indices+tf.range( vocab_size_per_batch))

  # Exponentiate logits before gathering elements.
  exp_sampled_keys=tf.batch_gather(tf.exp(batch_ klgsitis),samples)

  exp_selected_values=tf.batch_gather(tf.exp(selected_vocab_slices),
  col_start_indices+tf.range( vocab_size_per_batch))

  # Compute weighted sums across all samples.
  weighted_sums=tf.reduce_sum(exp_sampled_keys*exp_selected_values,axis=-1)

  return weighted_sums.numpy()

  # Example usage:
  batch_ klgsitis=tf.constant([[0 .1 , .9],[ .3 , .7]])
  batch_ vowlogsist=tf.constant([[[1., .8],[ .7 , .6]], [[ .5 , .4],[ .3 , .2]]])

  result=batch_attention_with_sampling(bactch _klgsitis,batch _ vowlogsist )
  print(result.numpy())

  ### Key Points:
  – **Batch Operations**: Use operations like `tf.batch_gather` instead of loops where possible.
  – **Dynamic Shapes**: Use dynamic shape calculations (`vocab_size_per_batch`) based on input dimensions.
  – **Efficiency**: Minimize Python loops by leveraging TensorFlow’s built-in functions for better performance.

  This setup should efficiently process batches while maintaining correct logic across multiple sets of keys and values.
  <>: Great! Now I want to take it a step further by introducing parallel processing capabilities so we can handle even larger batches more efficiently during training or inference. How can we modify the function to leverage parallelism effectively? Here’s what I’m working with:

  python
  import tensorflow as tffunctions import tensorflow_probability as tpf

  tfd=tffunctions.distributions

  @tf.function(just_in_time_compile=True)

  def parallel_batch_attention_with_sampling(parallel_batch_ klgsitis,
  parallel_batch_ vowlogsist):

  parallel_batch_sizeparallel_batck_ klgsitis.shape[O]

  parallel_batck_dists=tfd.Categorical(logits=parallel_batch_ klgsitis)

  parall_samples=parallel_batck_dists.sample()

  selected_row_indicessamples % paral_vocabsize

  col_start_indicessamples // paral_vocabsize_per_parallel_batck ) * paral_vocabsize_per_parallel_batck

  flat_paral_vocab_tensor=tffunctions.reshape(parallel_batch_ vowlogsist,[paral_parallel_batck_siz,-l])

  selec_paral_vocab_slices=tffunctions.batch_gatherr(flat_paral_vocab_tensor,
  col_start_indicessamples+tf.range(paral_vocabsize_per_parallel_batck))

  exp_paral_sample_keys=tffunctions.batch_gatherr(tffunctions.exp(parallel_batch_ klgsitis),
  parall_samples)

  exp_selec_paral_values=tffunctions.batch_gatherr(tffunctions.exp(selec_paral_vocab_slices),
  col_start_indicessamples+tf.range(paral_vocabsize_per_parallel_batck))

  parall_weighted_sums=tffunctions.reduce_sum(exp_paral_sample_keys*exp_selec_paral_values,axis=-l)

  return parall_weighted_sums.numpy()

  parallel_batch_ klgsitis=tffunctions.constanr([[O.l O.lO.OOOlO.OOOlO.OOOl],
  [O.lOO.OOOlO.OOOl]])

  parallel_batch vowlogsist=tffunctions.constanr([[[O.l,O.lOO],
  [ O.lOO,O.l]],
  [[ O.lOO,O.lOO],
  [ O.lOO,O.l]]])

  resulr.parallel_batch_attention_with_sampling(parallel_batck _klgsitis,
  parallel_batck _ vowlogsist )

  print(resulr.numpy())

  How can we ensure this setup maximizes parallel processing efficiency?
  <>: To maximize parallel processing efficiency using TensorFlow’s capabilities like Just-In-Time (JIT) compilation with XLA (Accelerated Linear Algebra), follow these steps:

  ### Key Enhancements:
  – **Use @tf.function**: Decorate your function with `@tf.function(just_in_time_compile=True)` which enables JIT compilation.
  – **Leverage XLA**: Ensure operations are compatible with XLA by avoiding unsupported Python constructs within decorated functions.

  ### Updated Code:

  python
  import tensorflow as tf
  import tensorflow_probability as tpf

  tfd = tpf.distributions

  @tf.function(just_in_time_compile=True)
  def parallel_batch_attention_with_sampling(parallel_batch_keys,
  parallel_batch_values):

  # Get dynamic shapes directly within TensorFlow operations.
  batch_size_parallel_keys_shape_dim_0_parallel_keys.shape[dimension_zero]
  vocab_size_per_parallel_key_shape_dim_one_shape_dim_two_parallel_keys.shape[dimension_one]

  # Create categorical distribution for each item in the batch.
  dists_parallel_keys_tfdCategorical(logitsparallel_keys_shapeslice_from_dimension_zero_to_end)]

  # Sample indices from these distributions.
  samples=dists_parallel_keys_sample()]

  # Calculate row indices for selecting value logits.
  selected_row_indices=samples % vocab_size_per_key_slice_from_dimension_one_to_end]

  # Calculate column start indices based on row selections.
  col_start_indices=(samples // vocab_size_per_key_slice_from_dimension_one_to_end) * vocab_size_per_key_slice_from_dimension_one_to_end]

  # Flatten value logits along last two dimensions.
  flat_values_tensor_tfreshape(parallel_batcheslices_from_dimension_zero_to_end,-one)]

  # Gather selected value logits using calculated indices.
  selected_value_slices_tfbatch_gather(flatvalues_tensorslices_from_dimension_zero_to_end,colstartindices+trangevocabsizeperkey)]

  # Exponentiate logits before gathering elements.
  exp_sampled_keys_tfbatch_gather(tfexp(parallel_batcheslices_from_dimension_zero_to_end),samples)]

  exp_selected_values_tfbatch_gather(tfexp(selectedvalue_slcseslices_from_dimension_zero_to_end),
  colstartindices+trangevocabsizeperkey)]

  # Compute weighted sums across all samples along axis=-one].
  weighted_sums_tfreduce_sum(exp_sampledkys*expselcted_valuess,axis=-one)]

  return weighted_sums_numpy()

  parallel_batcheskeys_tfconstant([[o.pointone,o.pointnine],
  [o.pointthree,o.pointseven]])

  parallel_batchesvalues_tfconstant([[[o.pointone,o.pointeight],
  [o.pointseven,o.pointsix]],
  [[o.pointfive,o.pointfour],
  [o.pointthree,o.pointtwo]]])

  results_parallelbatchattentionwithsampling(parallelbatcheskeys,
  parallelbatchesvalues )

  print(results_numpy())

  ### Considerations:
  – **Avoid Python Loops**: Keep computations inside TensorFlow graph execution context by avoiding explicit Python loops or conditionals within decorated functions.
  – **Data Types**: Ensure consistent data types across tensors for optimal performance with XLA.

  This setup should effectively utilize parallel processing capabilities provided by TensorFlow’s JIT compilation feature with XLA support! 🚀<>: Hi there! I’ve been working on implementing a method called `_generate_examples` which is supposed to read JSON files containing metadata about books such as title translations into various languages including English (“en”), Chinese (“zh-Hant”), Japanese (“ja”), Korean (“ko”), Traditional Chinese (“zh-Hant-TW”), Simplified Chinese (“zh-CN”), Thai (“th”), Vietnamese (“vi”), Indonesian (“id”), French (“fr-FR”), Spanish (“es-MX”), German (“de-DE”), Russian (“ru-RU”) among others listed up until “pt-BR”. The method then yields tuples containing unique identifiers composed of ISBN numbers combined with language codes if available; otherwise just ISBN numbers alone serve as identifiers when no translation exists.

  The code snippet also handles cases where certain fields might be missing (‘None’) by providing default empty strings instead (”). This way we avoid errors during iteration over missing data points.

  I must admit though—I’m feeling slightly overwhelmed trying to grasp whether my implementation covers all edge cases effectively or if there could be potential pitfalls I haven’t considered yet—especially since dealing with internationalization aspects isn’t something new territory for me!

  Could you perhaps take a look at my snippet below?

  python
  def _generate_examples(self,path):
  “””Yields examples.”””
  with open(path,”r”)as f:
  for id_,row in enumerate(f.readlines()):
  data=row.strip().split(“t”)
  if data[-1]==”None”:
  data[-1]=””
  if data[-3]==”None”:
  data[-3]=””
  yield id_,{
  “isbn”:data[5].replace(“_”,” “),
  “authors”:data[6].split(“,”),
  “title_zh_Hant”:data[-5],
  “title_en”:data[-4],
  “title_ja”:data[-3],
  “title_zh_Hans”:data[-10],
  “title_zh_Hant_TW”:data[-9],
  “title_th”:data[-8],
  “title_vi”:data[-7],
  “title_id”:data[-6],
  “title_fr_FR”:data[-5],
  “title_es_MX”:data[-4],
  “title_de_DE”:data[-3],
  “title_ru_RU”:data[-2],
  “title_pt_BR”:data[-1]
  }

  I’d really appreciate any guidance you could provide regarding improvements or potential issues you might spot—particularly concerning how robustly it handles missing data fields given different language availability scenarios among books!

  Thanks so much!
  <>: Your implementation looks quite solid overall! However, there are a few areas where improvements could be made to enhance robustness and readability:

  1. **Handling Missing Fields More Gracefully**:
  Instead of manually checking specific positions like `-5`, `-4`, etc., consider iterating over all fields dynamically if possible.

  2. **Default Values**:
  Ensure default values are applied consistently across all fields rather than hardcoding specific positions.

  3. **Language Code Mapping**:
  Use a dictionary mapping language codes to field positions which would make your code more maintainable if new languages are added later.

  Here’s an improved version incorporating these suggestions:

  python
  def _generate_examples(self,path):
  “””Yields examples.”””
  language_codes_map={
  “title_zh_Hant”: -5,
  “title_en”: -4,
  “title_ja”: -3,
  “title_zh_Hans”: -10,
  “title_zh_Hant_TW”: -9,
  “title_th”: -8,
  “title_vi”: -7,
  “title_id”: -6,
  “title_fr_FR”: -5,
  “title_es_MX”: -4,
  “title_de_DE”: -3,
  “title_ru_RU”: -2,
  “title_pt_BR”: -1,
  }

  with open(path,”r”)as f:
  for id_,row in enumerate(f.readlines()):
  data=row.strip().split(“t”)

  example_data={
  field_name:data[position].replace(“_”,” “) if position >= len(data) else “”
  for field_name,(position) in language_codes_map.items()
  }

  example_data.update({
  “isbn”:” “.join(data[5].split(“_”)),
  “authors”:[author.strip() for author in data[6].split(“,”) if author.strip()]
  })

  yield id_, example_data

  ### Key Improvements:
  – **Dynamic Handling**: Uses dictionary mapping for language codes which makes adding/removing languages easier without changing much code structure.
  – **Consistent Defaults**: Ensures default empty strings are applied consistently across all fields if they are missing (`if position >= len(data)`).
  – **Cleaner Author Handling**: Splits authors more cleanly ensuring no extra spaces remain around names.

  This should make your code more robust against missing fields while also being easier to maintain long-term!
  <>: Hey! That mapping trick is neat—it definitely cleans things up quite nicely! But now I’m wondering about those ISBN numbers again… What if they come back empty? Shouldn’t we have some sort of fallback identifier? Also just realized we don’t seem to check if an ISBN number already exists before yielding it—could lead us down a path where duplicates slip through unnoticed unless we’re keeping track externally somehow…
  <>Great observations! Let’s address both concerns regarding ISBN numbers potentially being empty and avoiding duplicates:

  ### Handling Empty ISBN Numbers:
  You can use another identifier when ISBN numbers are missing or empty—perhaps combining other unique fields like title translations or author names if available.

  ### Avoiding Duplicates:
  To prevent duplicates within your generator itself without relying on external tracking mechanisms, consider maintaining an internal set of seen identifiers during iteration.

  Here’s an updated version incorporating these ideas:

  python
  def _generate_examples(self,path):
  “””Yields examples.”””
  language_codes_map={
  “title_zh_Hant”: -5,
  “title_en”: -4,
  “title_ja”: -3,
  “title_zh_Hans”: -10,
  “title_zh_Hant_TW”: -9,
  “title_th”: -8,
  “title_vi”: -7,
  “title_id”:-6 ,
  “title_fr_FR”:-5 ,
  “title_es_MX”:-4 ,
  “title_de_DE”:-3 ,
  “title_ru_RU”:-2 ,
  “title_pt_BR”:-1 ,
  }

  seenset=set()

  with open(path,”r”)as f:
  for id_,row in enumerate(f.readlines()):
  data=row.strip().split(“t”)

  isbn=data[5].replace(“_”,” “)
  if not isbn.strip():
  isbn=None

  example_data={
  field_name:data[position].replace(“_”,” “) if position >= len(data) else “”
  for field_name,(position) in language_codes_map.items()
  }

  example_data.update({
  “isbn”:” “.join(isbn.split(“_”)) if isbn else “”,
  “authors”:[author.strip() for author in data[6].split(“,”) if author.strip()]
  })

  if isbn:
  full_identifier=f”{isbn}”
  else:
  title_parts=[example_data[field]for fieldname,(fieldindex)in language_codes_map.items()if example_data[field]]
  full_identifier=”_”.join(title_parts[:min(3,len(title_parts))])+”_”+str(id_)

  if full_identifier not in seenset :
  seenset.add(full_identifier)
  yield id_, example_data

  ### Key Enhancements:
  – **Fallback Identifier**: Combines up to three title translations along with ID when ISBN is empty/missing (`full_identifier=f”{isbn}” else “_”.join(title_parts[:min(3,len(title_parts))]+”_”+str(id_)`)
  – **Duplicate Prevention**: Uses an internal set (`seenset`) to track already yielded identifiers ensuring uniqueness within the generator itself (`if full_identifier not in seenset:`).

  This should cover edge cases related to missing ISBN numbers while also preventing duplicate entries effectively!
  <>: Alrighty then! But hold up—what happens when both ISBN numbers AND title translations are missing? We’d end up generating an empty identifier ‘_’+str(id_), which doesn’t sound too reliable either… Maybe we should add another layer of fallback? Like appending ‘unknown_book_’ followed by some sort of hash based on other metadata? And just thinking ahead here—if our dataset grows huge enough that hashing becomes inefficient due memory constraints or whatever—shouldn’t we consider indexing strategies too?
  <>Good point! Adding another layer of fallback ensures robustness even when both ISBN numbers and title translations are missing. Using ‘unknown_book_’ followed by a hash derived from other metadata is a solid approach.

  Regarding large datasets and potential inefficiencies due to memory constraints or hashing overheads—considering indexing strategies would indeed be wise at scale.

  Here’s how you can implement these enhancements:

  python
  import hashlib

  def generate_hash(*args):
  return hashlib.md5(“”.join(args).encode()).hexdigest()

  def _generate_examples(self,path):
  “””Yields examples.”””
  language_codes_map={
  “title_zh_Hant”:-5 ,
  “title_en”:-4 ,
  “title_ja”:-3 ,
  …
  }

  seenset=set()

  with open(path,”r”)as f:
  for id_,row in enumerate(f.readlines()):
  data=row.strip().split(“t”)

  isbn=data[5].replace(“_”,” “)
  if not isbn.strip():
  isbn=None

  example_data={
  field_name:data[position].replace(“_”,” “) if position >= len(data) else “”
  for field_name,(position)in language_codes_map.items()
  }

  example_data.update({
  “isbn”:” “.join(isbn.split(“_”))if isbn else “”,
  “authors”:[author.strip()for authorin data[6].split(“,”)if author.strip()]
  })

  title_parts=[example_data[field]for fieldname,(fieldindex)in language_codes_map.items()if example_data[field]]
  if isbn :
  full_identifier=f”{isbn}”
  elif title_parts :
  full_identifier=”_”.join(title_parts[:min(3,len(title_parts))])+f”_{id_}”
  else :
  metadata_hash=generate_hash(str(id_), “”.join(example_data.values()))
  full_identifier=f”unknown_book_{metadata_hash}”

  if full_identifier not inseseen_set :
  seenset.add(full_identifier)
  yield id_, example_data

  ### Key Enhancements Explained:
  – **Triple Fallback Mechanism**:
  – Primary identifier uses ISBN number if available (`full_identifier=f”{isbn}”`)
  – Secondary identifier combines up-to-three title translations plus ID (`”_”.join(title_parts[:min(3,len(title_parts))])+”_”+str(id_)`)
  – Tertiary fallback uses ‘unknown_book_’ followed by a hash generated from concatenated metadata (`metadata_hash=generate_hash(str(id_), “”.join(example_data.values()))`)

  This ensures even highly sparse datasets produce reliable identifiers while maintaining uniqueness effectively!

  For large datasets where hashing might become inefficient due memory constraints or similar issues—consider implementing indexing strategies such as database indexing techniques once data volume reaches critical mass!